Reinforcement learning [1]} considers the problem of a learner or agent embedded in an environment, where the agent must progressively improve the actions it selects in response to each environmental situation or state (Figure  REF a). Critically, in contrast to supervised learning, the agent does not receive explicit feedback directly indicating correct actions. Instead, each action elicits a signal of associated reward or lack of reward, and the reinforcement learning problem is to progressively update behavior so as to maximize the reward accumulated over time. Because the agent is not told directly what to do, it must explore alternative actions, accumulating information about the outcomes they produce, thereby gradually honing in on a reward-maximizing behavioral policy.
